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TimesFM Forecasting

Zero-shot univariate time series forecasting with Google's TimesFM foundation model, including calibrated prediction intervals.

Data & AnalyticsIntermediate33,0303,248AI score 9/10Last updated: Aug 9, 2026

What it does

  • Runs zero-shot forecasting on any univariate series using Google Research's TimesFM 2.5 (200M params) — no model training needed.
  • Returns both a point (median) forecast and 10 quantile bands (q10–q90) so you can build 80%/90% prediction intervals.
  • Accepts CSV, pandas DataFrame, or NumPy array inputs and supports batch forecasting of hundreds or thousands of series.
  • Ships a mandatory preflight checker (scripts/check_system.py) that validates RAM, GPU VRAM, disk space, and Python version before the ~800 MB weights are downloaded.
  • Documents covariate forecasting (forecast_with_covariates()), quantile-based anomaly detection, ARIMA baseline comparison, and 10 common pitfalls.

Who it's for

  • Analysts and data scientists forecasting sales, demand, sensors, energy, vitals, or weather.
  • Teams who want a fast foundation-model baseline instead of hand-tuning ARIMA/ETS.
  • Inventory and capacity planners who need uncertainty bands, not just a single number.
  • Not a fit if you need interpretable statistical coefficients, series classification/clustering, or multivariate causality analysis.

Example uses

  1. 12-month sales forecast: load monthly_sales.csv, convert each column into a 1-D array, call forecast(horizon=12), and emit a table with 80% lower/upper bounds.
  2. Sensor anomaly alerts: flag actuals outside the 80% interval as warnings and outside the 90% interval as critical.
  3. Promo-aware demand forecast: pass price and holiday covariates via forecast_with_covariates() and compare MAE against plain TimesFM and an ARIMA baseline.

· · · Install guide · · ·

Try it now, no install

Paste this into Claude to use the skill without installing anything.

Read the instructions in this file and follow them to help me:
https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/HEAD/skills/timesfm-forecasting/SKILL.md

What I want: (describe your task here)

If Claude can't open the link, open it yourself and paste the contents instead.

If it works for you, download the ZIP below and install it. Then it runs on its own — no pasting each time.

Install in the Claude app (no terminal)
  1. Download the ZIP with the button below.
  2. In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
  3. Go to Customize → Skills → + → 'Upload a skill' and upload the ZIP.
Download ZIP
Install in Claude Code

Let Claude do it — paste this into Claude Code

Install the skill I found on Claude Skill Mart.
Copy the skills/timesfm-forecasting folder from the GitHub repo K-Dense-AI/scientific-agent-skills into my ~/.claude/skills/timesfm-forecasting/.
When it's done, tell me in one line what this skill can do.

Install with a command instead

git clone https://github.com/K-Dense-AI/scientific-agent-skills.git && mkdir -p ~/.claude/skills && cp -r scientific-agent-skills/skills/timesfm-forecasting ~/.claude/skills/

This is a third-party skill. Check the source repository before installing.

  1. Open a terminal and clone the repo: git clone https://github.com/K-Dense-AI/scientific-agent-skills.git
  2. Copy the skill into your Claude skills folder: mkdir -p ~/.claude/skills && cp -r scientific-agent-skills/skills/timesfm-forecasting ~/.claude/skills/
  3. Confirm Python 3.10+: python --version
  4. Run the mandatory preflight check: python ~/.claude/skills/timesfm-forecasting/scripts/check_system.py (aim for ≥4 GB free RAM and ≥2 GB free disk)
  5. Install TimesFM: uv pip install "timesfm[torch]" (or pip install "timesfm[torch]")
  6. Install PyTorch for your hardware: NVIDIA GPU → pip install torch --index-url https://download.pytorch.org/whl/cu121; CPU-only → .../whl/cpu; Apple Silicon → default install works.
  7. Verify: python -c "import timesfm; print(timesfm.__version__)"
  8. Restart Claude Code and ask something like "forecast the next 12 months from this sales CSV." The first run downloads ~800 MB of weights from HuggingFace into ~/.cache/huggingface/.